Instructions to use JovialValley/model_broadclass_onSet3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JovialValley/model_broadclass_onSet3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="JovialValley/model_broadclass_onSet3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("JovialValley/model_broadclass_onSet3") model = AutoModelForCTC.from_pretrained("JovialValley/model_broadclass_onSet3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JovialValley/model_broadclass_onSet3: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/JovialValley/model_broadclass_onSet3/resolve/main/training_args.bin
- Command line
-
hf download hf://JovialValley/model_broadclass_onSet3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JovialValley/model_broadclass_onSet3/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- c066bfc5199bf00a5b43201490b33933df14835a83914d182f8d85fdc3afaa46
- Size of remote file:
- 3.45 kB
- SHA256:
- 58d22e8612a1c4ca37f6951ec2a4e6562fd744fa8c1c91373218e82e5a6d29a8
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